Identification of pure particle types in water treatment by electrical sensing zone method combined with machine learning

PU Yitao; XIAO Kang; WANG Xiaodong; YANG Ruyue; WANG Yuxuan; KE Shuizhou; GAO Jingsi

doi:10.13205/j.hjgc.202511001

Volume 43 Issue 11

Nov. 2025

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PU Yitao, XIAO Kang, WANG Xiaodong, YANG Ruyue, WANG Yuxuan, KE Shuizhou, GAO Jingsi. Identification of pure particle types in water treatment by electrical sensing zone method combined with machine learning[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(11): 1-10. doi: 10.13205/j.hjgc.202511001

Citation:

PU Yitao, XIAO Kang, WANG Xiaodong, YANG Ruyue, WANG Yuxuan, KE Shuizhou, GAO Jingsi. Identification of pure particle types in water treatment by electrical sensing zone method combined with machine learning[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(11): 1-10. doi: 10.13205/j.hjgc.202511001

Citation:

PU Yitao, XIAO Kang, WANG Xiaodong, YANG Ruyue, WANG Yuxuan, KE Shuizhou, GAO Jingsi. Identification of pure particle types in water treatment by electrical sensing zone method combined with machine learning[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(11): 1-10. doi: 10.13205/j.hjgc.202511001

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Identification of pure particle types in water treatment by electrical sensing zone method combined with machine learning

doi: 10.13205/j.hjgc.202511001

1. College of Civil Engineering, Hunan University, Changsha 410082, China;
2. College of Materials and Environmental Engineering, Shenzhen Polytechnic, Shenzhen 518055, China;
3. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China;
4. Beijing Yanshan Earth Critical Zone National Research Station, Beijing 101408, China;
5. College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 101408, China;
6. Sino-Danish College, University of Chinese Academy of Sciences, Beijing 101408, China

Received Date: 2024-07-17
Accepted Date: 2024-08-31
Rev Recd Date: 2024-08-21

Available Online: 2026-01-09

Abstract

Abstract

The accurate detection of particle concentration and size distribution is crucial for ensuring the efficient operation of water treatment plants and environmental monitoring. However， in practical environments， various particles are often mixed， making it challenging for existing detection methods to achieve precise measurements in complex particle systems. To address the need for intelligent monitoring in water treatment plants， this study proposed an approach that integrated the electrical sensing zone （ESZ） method with machine learning （ML）， achieving high-precision classification and identification of typical pure-substance particles in water treatment for the first time. By optimizing ESZ acquisition conditions， the optimal parameters were determined as a suction speed of 3 mL/min and a stirring speed of 300 r/min. The formation mechanism of ESZ signals was theoretically analyzed， revealing that multiple factors exist， including fluid velocity， particle diameter， density， and shape， influence waveforms. A pure-substance particle database was constructed based on ESZ， and various machine learning classification algorithms were compared. The support vector machine （SVM） model demonstrated the best performance， achieving an identification accuracy of 95.3% for four pure-substance particles： bubbles， quartz sand， polyethylene terephthalate （PET）， and paramecium. While bubbles and quartz sand were distinguished with excellent accuracy， some confusion remained between PET and paramecium， indicating the need for further algorithm optimization. This study provides a new approach for particle identification， and future work will involve validation in mixed-particle systems， offering technical support for the precision and intelligence of water treatment monitoring.
- electrical sensing zone method,
- machine learning,
- pure substance particles,
- species identification,
- support vector machine （SVM）

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References(40)

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